The page below lists the coming and past seminars, and provides a link to the presentations that you may have missed. Click on a presentation title for the abstract.
Alert emails are sent to the TAU team and to the announcement mailing-list tau-seminars to which anyone can subscribe by clicking here.
Some of these presentations are organized with the GT Deep Net; to subscribe to the related announcement mailing-list, click there.
From October 2019 the seminars will take place on Friday mornings at 11 am in room 2014 (building 660), unless specified otherwise.
The presentations are recorded and available here.
- Friday, 29th of November, 11h: Luigi Gresele (Max Planck Institute for Intelligent Systems and Biological Cybernetics, Tübingen): The Incomplete Rosetta Stone Problem: Multi-View Nonlinear ICA, with applications to neuroimaging
- Friday, 15th of November, 11h: Balthazar Donon (TAU): Graph Neural Solvers for Power Systems
- Tuesday, 12th of November, 14h30: Jonathan Raiman (OpenAI / TAU): DeepType: résolution référentielle d’entités multilingues par l’évolution de systèmes de types neuronaux
- Friday, 8th of November, 11h: Mandar Chandorkar (TAU / Centrum Wiskunde & Informatica (CWI), Amsterdam): Dynamic Time Lag Regression: Predicting What & When
- Friday, 11th of October, 11h: Signe Riemer-Sørensen (SINTEF digital, Norway): Machine learning in the real world
- Monday, 7th of October, 15h (Amphi Shannon): Corentin Tallec (TAU)'s PhD defense
- Thursday, 3rd of October, 14h30: Lisheng Sun (TAU): Meta-learning as a Markov Decision Process (MDP)
- Tuesday, 1st of October, 14h: Jakob Runge (German Aerospace Center, Institute of Data Science, Jena): Perspectives for causal inference on time series in Earth system sciences
- Thursday, 12th of September, 14h30: Victor Berger (TAU): From abstract items to latent space to observed data and back: Compositional Variational Auto-Encoder, followed by Zhengying Liu (TAU): Overview and unifying conceptualization of Automated Machine Learning & AutoCV Challenges Analysis
- Wednesday, 11th of September: DataIA day on Safety & AI, Turing building (INRIA Saclay); at 11h45 - 12h15: Julien Girard (CEA-list/TAU): Building Specifications for Perception Systems: Formal Proofs of Deep Networks Trained with Simulators
- Tuesday, 10th of September, 14h: Guillaume Doquet (TAU): Agnostic Feature Selection, followed by Pierre Wolinski (TAU): Learning with Random Learning Rates
- Tuesday, 3rd of September, 14h30: Luca Veyrin-Forrer (TAU): Learning To Run A Power Network
- Tuesday, 2nd of July, 14h30: Reda Alami (Orange/LRI): Memory bandits for decision-making in dynamic environments. Application to 5G optimization.
- Tuesday, 25th of June, 15h15: Victor Berger (TAU): Ensemblist Variational AutoEncoder: latent representation of semi-structured data, and Zhengying Liu (TAU): AutoCV Challenge Design and Baseline Results
- Tuesday, 18th of June, 14h30: Guillaume Doquet (TAU): Agnostic Feature Selection/ Sélection d'attributs agnostique
- Tuesday, 11th of June, 14h30: Ada Altieri (LPT, ENS): Introduction to the Thouless-Anderson-Palmer formalism and recent applications
- Tuesday 4th to Friday 7th of June (at ENS Cachan): summer school / workshop on Machine Learning & Formal Methods, details here
- Tuesday, 28th of May, 14h30: Talk canceled
- Wednesday 15th of May, 14h30: Erol Gelenbe (Imperial College): Réseaux Neuronaux Aléatoires - Solutions en Forme Produit, Apprentissage et Apprentissage Profond, Applications
- Tuesday, 14th of May, 14h30: Laurent Daudet (LightOn / Paris Diderot): Optical random features for large-scale machine learning
- Tuesday 7th of May, 14h30: Thibault Groueix & Pierre Alain Langlois(Imagine, ENPC): Deep Learning for 3D - Toward surface generation
- [DataIA] Thursday 2nd of May, 14h (Nano-Innov): Freddy Lecue (Chief AI Scientist @Thales Canada / INRIA Wimmics team): XAI - The story so far
- Tuesday, 23rd of April, 14h30: Julien Hay & Bich-Liên Doan (CentraleSupelec/LRI): Personnalisation de la recommandation d’articles d’actualité
- Thursday, 18th of April, 14h (salle des thèses 435, bâtiment 650): Big data, IA, sélection des données: causalités, corrélations, conséquences
- Diviyan Kalainathan: Causalité observationnelle, découverte de liens de cause à effet sans expériences randomisées
- Paola Tubaro: Sélectionné.e par une IA ? Algorithmes, inégalités, et les « humains dans la boucle »
- Tuesday, 16th of April, 14h30: Michele Alessandro Bucci (LIMSI): Control of chaotic dynamical system with Deep Reinforcement Learning approach
- Tuesday, 26th of March, 14h30 (usual room R2014): Saumya Jetley (University of Oxford): DeepInsight - An examination of the class decision functions learned by deep nets
- Tuesday, 12th of March, 14h30 (usual room R2014): Alexis Dubreuil (Group for Neural Theory, ENS): Reverse-engineering of low-rank recurrent neural networks
- Tuesday, 5th of March, 14h30 (usual room R2014): Gwendoline de Bie (ENSAE/TAU): Stochastic Deep Networks
- Tuesday, 26th of February, 14h30 (usual room R2014): Jean Barbier (ENS Paris & ICTP Trieste): Phase transitions in high-dimensional estimation and learning
- Wednesday, 20th of February, 11h30 (amphithéâtre Sophie Germain, Turing building, INRIA Saclay): [DataIA] Jérémie Mary (Critéo / Univ. Lille) Online advertising and strategic bidding
- Tuesday, 19th of February, 14h30 (usual room R2014): Loris Felardos (TAU team / IBPC): An Introduction to Graph Neural Networks
- For information: Friday, 15th of February: GT PASADENA seminar day
- Wednesday, 13th of February, 9h (Shannon amphitheatre, 660 building): Benjamin DONNOT's PhD defense: Deep Learning Methods for Predicting Flows in Power Grids: Novel Architectures and Algorithms
- For information: Wednesday, 30th of January, at IHES (full day): Statistics/Learning at Paris-Saclay
- Wednesday, 16th of January, 14h30 (usual room R2014): Corentin Tallec & Léonard Blier (TAU): T.B.A.
- Tuesday, 15th of January, 14h30 (amphi Shannon): Laurent Basara (TAU): The TrackML challenge: concept, methods and approaches
- Monday, 7th of January, 10h30 (usual room R2014): Jonathan Raiman (OpenAI): OpenAI Five: Atteindre un niveau professionnel à Dota en jouant contre soi-même
- Friday, 14th of December, 14h30 (usual room R2014): [ GT DeepNet ] Edouard Oyallon (CentraleSupelec): The shallow learning quest
- Thursday, 13th of December, 11h11 (usual room R2014): Julien Girard (TAU/CEA-list): A short introduction to formal methods and their applications for Robust Deep networks
- Thursday, 22nd of November, 11h11 (usual room R2014): Adrian Alan Pol (CERN): Machine Learning applications to CMS Data Quality Monitoring
- Thursday, 15th of November, 11h11 (usual room R2014): Philippe Esling (IRCAM): Artificial creative intelligence: variational inference and deep learning for modeling musical creativity slides
- Wednesday, 17th of October, 14h30 (Shannon amphitheatre): Pan Zhang (Institute of Theoretical Physics, Chinese Academy of Sciences): Solving Statistical Mechanics using Variational Autoregressive Networks
- Friday, 5th of October, 11h30 (usual room R2014): Thomas Lucas (Toth team, INRIA Grenoble): Mixed batches and symmetric discriminators for GAN training
- Thursday, 6th of September, 14h30 (usual room R2014): Mo Yang (TAU/CDS-LAL)'s end of internship: Prediction of storm trajectories
- Friday, 29th of June, 16h (Shannon amphitheatre): Thomas Schmitt (TAU)'s PhD defense: Appariements Collaboratifs des Offres et Demandes d'Emploi
- Thursday, 28th of June, 14h30 (Shannon amphitheatre): Alexandre Aussem (LIRIS - Lyon): Identifying irreducible disjoint factors in multivariate probability distributions: Application to multilabel learning
- Friday, 22nd of June, 11h (Shannon amphitheatre): Peter Bosman (CWI, Delft): Gene-pool Optimal Mixing Evolutionary Algorithms - From Foundations to Applications
- Friday, 22nd of June, 8h30 - 17h (room 1046): Isabelle Guyon's group seminar day: MEDI-CHAL / L2RPN
- June, Tuesday 12th (Shannon amphitheatre): Bérénice Huquet, Amandine Pierrot, Georges Hébrail (EDF Lab Paris-Saclay): Non-Intrusive Load Monitoring (NILM) problems and studies at EDF R&D
- June, Thursday 7th (17h): PhD seminar: Zhengying Liu: No Free Lunch Theorems
- June, Tuesday 5th: Martin Toth (TAU/CentraleSupelec): Deep Learning for skin disease diagnosis assistance
- May, Thursday 31st (Shannon Amphitheatre, 14h30): François Gonard's PhD defense: Cold-start recommendation: from algorithm portfolios to job applicant matching
- May, Wednesday 30th: Diviyan Kalainathan: Tutorial on Docker
- May, Tuesday 29th: Yufei Han (Symantec Research labs): Multi-label Learning with Highly Incomplete Data via Collaborative Embedding
- May, Thursday 24th: Stuart Russell (UC Berkeley): Provably Beneficial Artificial Intelligence, at the DATAIA Institute (Turing building, 11am)
- May, Monday 7th: Jean-Noël Vittaut (Paris 8): General Game Playing pour les jeux à information parfaite ou imparfaite
- May, Friday 4th, 11h: Dominique Fourer (IRCAM): Analysis of non-stationary and multicomponent signals with applications to music information retrieval
- April, Wednesday 25th: Joon Kwon (CMAP): Mirror descent strategies for regret minimization and approachability
- April, Tuesday 17th: Bertrand Thirion (Parietal team, Neurospin, INRIA/CEA): Statistical inference for high-dimensional data & application to brain imaging
- April, Tuesday 10th: Berna Bakir Batu (TAU team): A Reinforcement Learning Approach for Simulating Cascading Failures in Power Grids
- April, Tuesday 3rd: Benjamin Donnot (TAU team): Fast Power system security analysis with Guided Dropout
- March, Tuesday 27th: Nizam Makdoud (TAU team): Intrinsic Motivation, Exploration and Deep Reinforcement Learning
- March, Tuesday 20th: Hugo Richard (Parietal/TAU teams, INRIA): Data based analysis of visual cortex using deep features of videos (more information...)
- March, Tuesday 13th: David Rousseau (Laboratoire de l'Accélérateur Linéaire (LAL), Orsay): TrackML : The High Energy Physics Tracking Challenge (more information...)
- March, Tuesday 6th: Ulisse Ferrari (Institut de la Vision): Neuroscience & big-data: Collective behavior in neuronal ensembles (more information...)
- March, Friday 2nd: François Landes (IPhT): Physicists using and playing with Machine Learning tools: two examples (more information...)
- February, Tuesday 27th: Wendy Mackay (INRIA/LRI ExSitu team): Human-Computer Partnerships: Leveraging machine learning to empower human users (more information...)
- February, Tuesday 20th: Jérémie Sublime (ISEP): Unsupervised learning for multi-source applications and satellite image processing (more information...)
- February, Friday 16th: Rémi Leblond (INRIA Sierra team): SeaRNN: training RNNs with global-local losses (more information...)
- February, Tuesday 13th: Zoltan Szabo (CMAP & DSI, École Polytechnique): Linear-time Divergence Measures with Applications in Hypothesis Testing (more information...)
- January, Tuesday 23rd (usual room 2014): Olivier Goudet & Diviyan Kalainathan (TAU): End-to-end Causal Generative Neural Networks (more information...)
- January, Friday 19th, whole day (IHES): workshop stats maths/info du plateau de Saclay (more information...)
- January, Tuesday 9th (room 435, "salle des thèses", building 650): Michèle Sébag & Marc Schoenauer (TAU): Stochastic Gradient Descent: Going As Fast As Possible But Not Faster (more information...)
- December, Tuesday 19th, 14:30 (room 455, building 650): Antonio Vergari (LACAM, University of Bari 'Aldo Moro', Italy): Learning and Exploiting Deep Tractable Probabilistic Models (more information...)
- December, Wednesday 13th, 14:30 (room 445, building 650): Robin Girard (Mines ParisTech Sophia-Antipolis): Data mining and optimisation challenges for the energy transition (more information...)
- December, first week: break (NIPS)
- November, Wednesday 22th, 14:30 (room 2014): Marylou Gabrié (ENS Paris, Laboratoire de Physique Statistique): Mean-Field Framework for Unsupervised Learning with Boltzmann Machines (more information...)
- November, Friday 17th, 11:00 (Shannon amphitheatre): [ GT DeepNet ] Levent Sagun (IPHT Saclay): Over-Parametrization in Deep Learning (more information...)
- November, Wednesday 15th, 14:30 (room 2014): Diviyan Kalainathan & Olivier Goudet (TAU): Causal Generative Neural Networks (more information...)
- November, Thursday 9th, 11:00 (Shannon amphitheatre): Claire Monteleoni (CNRS-LAL / George Washington University): Machine Learning Algorithms for Climate Informatics, Sustainability, and Social Good (more information...)
- October, Tuesday 24th, 14:30 (Shannon amphitheatre): Benjamin Guedj (MODAL team, Inria Lille): A quasi-Bayesian perspective to NMF: theory and applications (more information...)
- October, Wednesday 18th, 14:30 (room 2014): Théophile Sanchez (TAU): End-to-end Deep Learning Approach for Demographic History Inference (more information...)
- October, Wednesday 11th, 14:00 (room 2014): Victor Estrade (TAU): Robust Deep Learning : A case study (more information...)
- October, Wednesday 4th, 14:30 (room 2014): Hugo Richard (Parietal/TAU): Data based alignment of brain fmri images (more information...)
- September, Tuesday 19th, 11:00 (Shannon amphitheatre): Carlo Lucibello (Politecnico di Torino): Probing the energy landscape of Artificial Neural Networks (more information...)
- July, Tuesday 4th, from 11:00 to 13:00 (Shannon amphitheatre): presentation of Brice Bathellier's team + MLspike by Thomas Deneux (more information...)
- June, Friday 30th, 14:30 (room 2014): internships presentation by Giancarlo Fissore: Learning dynamics of Restricted Boltzmann Machines, and by Clément Leroy: Free Energy Landscape in a Restricted Boltzmann Machine (RBM) (more information...)
- June, Thursday 29th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Alexandre Barachant: Information Geometry: A framework for manipulation and classification of neural time series (more information...)
- June, Tuesday 27th, 14:30 (room 2014) Réda Alami et Raphaël Féraud (Orange Labs): Memory Bandits : A bayesian Approach for the Switching Bandit Problem (more information...)
- June, Monday 12th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Romain Couillet (Centrale-Supélec): A Random Matrix Framework for BigData Machine Learning (more information...)
- May, Wednesday 24th, 16:00 (room 2014): Priyanka Mandikal (TAU): Anatomy Localization in Medical Images using Neural Networks (more information...)
- April, Friday 28th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Jascha Sohl-dickstein (Google Brain): Deep Unsupervised Learning using Nonequilibrium Thermodynamics (more information...)
- April, Tuesday 3rd: Thomas Schmitt: RecSys challenge 2017 (more information...)
- March, Thursday 2nd, 14:30 (Shannon amphitheatre): Marta Soare (Aalto University): Sequential Decision Making in Linear Bandit Setting (more information...)
- February 22nd, 11h: Enrico Camporeale (CWI): Machine learning for Space-Weather forecasting
- February, Thursday 16th (Shannon amphi.), 14h30: [ GT DeepNet ] Corentin Tallec: Unbiased Online Recurrent Optimization (more information...)
- February 14th (Shannon amphi.), 14h: [ GT DeepNet ] Victor Berger (Thales Services, ThereSIS): VAE/GAN as a generative model (more information...)
- January 25th, 10h30: Romain Julliard (Muséum National d'Histoire Naturelle): 65 Millions d'Observateurs (more information...)
- January 24th: Daniela Pamplona (Biovision team, INRIA Sophia-Antipolis / TAO): Data Based Approaches in Retinal Models and Analysis (more information...)
- November 30th: Martin Riedmiller (Google DeepMind). Deep Reinforcement learning for learning machines (more information...)
- November 29th: Amaury Habrard (Universite Jean Monnet de Saint-Etienne). Domain Adaptation with Optimal Transport: Mapping Estimation and Theory (more information...)
- November 24th: [ GT DeepNet ] Rico Sennrich (University of Edinburgh). Neural Machine Translation: Breaking the Performance Plateau (more information...)
- June 28th: Lenka Zdeborova (CEA,Ipht). Solvable models of unsupervised feature learning LRI_matrix_fact.pdf
- May 3rd: Emile Contal (ENS-Cachan). The geometry of Gaussian processes and Bayesian optimization. slides_semstat16.pdf
- April 26: Marc Bellemare (Google DeepMind). Eight Years of Research with the Atari 2600 (more information...)
- April 12: Mikael Kuusela (EPFL). Shape-constrained uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider.(more information...)
- March 22nd: Matthieu Geist (Supélec Metz): Reductions from inverse reinforcement learning to supervised learning (more information...)
- March 15: Richard Wilkinson (University of Sheffield): Using surrogate models to accelerate parameter estimation for complex simulators (more information...)
- March 1st: Pascal Germain (Université Laval, Québec): A Representation Learning Approach for Domain Adaptation (more information...)
- February 9th: François Dufour (INRIA Bordeaux) (more information...)
- January 26th: Laurent Massoulié: Models of collective inference.(more information...).
- January 19th: Sébastien Gadat: Regret bounds for Narendra-Shapiro bandit algorithms (more information...)..
- December 15th: Joon Kwon: SPARSE REGRET MINIMIZATION.(more information...).
- November 19th: Phillipe Sampaio: A derivative-free trust-funnel method for constrained nonlinear optimization (more information...).
- October 27: Audrey Durand: Bandits for healthcare (more information...).
- October 20th: Jean Lafond: Low Rank Matrix Completion with Exponential Family Noise (more information...).
- October 13th
- Sept. 28th
- Olivier Pietquin, Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games OlivierPietquin_ICML15.pdf
- Francois Laviolette, Domain Adaptation (slides soon)
- July 2nd:Alaa Saade:MaCBetH : Matrix Completion with the Bethe Hessian(more information...)
- June 15th: Claire Monteleoni:Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science
- June 2nd: Robyn Francon: Reversing Operators for Semantic Backpropagation
- May 18th:Andras Gyorgy:Adaptive Monte Carlo via Bandit Allocation
- April 28th:Vianney Perchet:Optimal Sample Size in Multi-Phase Learning(more information...)
- April 27th:Hédi Soula, TBA
- April 21th: Gregory Grefenstette, INRIA Saclay: Personal semantics(more information...)
- April 7th: Paul Honeine: Relever deux défis majeurs en apprentissage par méthodes à noyaux:problème de pré-image et apprentissage en ligne (more information...)
- March 31th: Bruno Scherrer (Inria Nancy): Non-Stationary Modified Policy Iteration (more information...)
- March 24th: Christophe Schülke(ESPCI): Community detection with modularity: a statistical physics approach (more information...)
- March 10th: Balazs Kegl: Rapid Analytics and Model Prototyping (more information...)
- February 24th: Madalina Drugan (Vrije Universiteit Brussel, Belgium): Multi-objective multi-armed bandits (more information...)
- February 20th: Holger Hoos (University of British Columbia, Canada): séminaire MSR - see the slides
- February 17th :Aurélien Bellet: The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization more information...
- February 10th, Manuel Lopes 15interlearnteach.pdf
- January 27th :Raphaël Baillyra: Tensor factorization for multi-relational learning ((more information...)
- January 13th : Francesco Caltagirone: On convergence of Approximate Message Passing (talk_Caltagirone.pdf)
- January 6th : Emilie Kaufmann: Bayesian and frequentist strategies for sequential resource allocation (Emilie_Kauffman.pdf)
- November 4th :Joaquin Vanschoren:OpenML: Networked science in machine learning
- Oct. 28th,
- Antoine Bureau, "Bellmanian Bandit Network"
-1- Manuel Lopes, Tobias Lang, Marc Toussaint, and Pierre-Yves Oudeyer. Exploration in model-based reinforcement learning by empirically estimating learning progress. In Neural Information Processing System (NIPS), 2012.
- Basile Mayeur
Taking inspiration from inverse reinforcement learning, the proposed Direct Value Learning for Reinforcement Learning (DIVA) approach uses light priors to gener- ate inappropriate behavior’s, and use the corresponding state sequences to directly learn a value function. When the transition model is known, this value function directly defines a (nearly) optimal controller. Otherwise, the value function is extended to the (state,action) space using off-policy learning.
The experimental validation of DIVA on the Mountain car shows the robustness of the approach comparatively to SARSA, based on the assumption that the tar- get state is known. Lighter assumptions are considered in the Bicycle problem, showing the robustness of DIVA in a model-free setting.
- Thomas Schmitt, "Exploration / exploitation: a free energy-based criterion"
- Oct. 14th, Holger Hoos Slides attached.
- Sept. 29th, Rich Caruana